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In recent years, with the increase in the volume and complexity of data, choosing a suitable database for storing huge amounts of data is not easy, because it must consider aspects such as manageability, scalability, and extensibility. Nowadays, the NoSQL databases have gained immense popularity for their efficiency in managing such datasets compared to relational databases. However, relational databases also exhibit some advantages in certain circumstances, therefore many applications use a combined approach: relational and non-relational. This paper performs a comparative evaluation of two popular open-source DBMSs: MySQL Document Store and Elasticsearch as non-relational DBMSs; this comparison is based on a detailed analysis of CRUD operations for different amounts of data showing how the databases could be modeled and used in an application. A case-study application was developed for this purpose in Java programming language and Spring framework using for data storage both relational MySQL and non-relational Elasticsearch and MySQL Document Store. To model the real situation encountered in several developed applications that use both relational and non-relational databases, a data replication solution that imports data from the primary relational MySQL database into Elasticsearch and MySQL Document Store as possible alternatives for more efficient data search was proposed and implemented.

Reactome is a free, open-source, open-data, curated and peer-reviewed knowledgebase of biomolecular pathways. One of its main priorities is to provide easy and efficient access to its high quality curated data. At present, biological pathway databases typically store their contents in relational databases. This limits access efficiency because there are performance issues associated with queries traversing highly interconnected data. The same data in a graph database can be queried more efficiently. Here we present the rationale behind the adoption of a graph database (Neo4j) as well as the new ContentService (REST API) that provides access to these data. The Neo4j graph database and its query language, Cypher, provide efficient access to the complex Reactome data model, facilitating easy traversal and knowledge discovery. The adoption of this technology greatly improved query efficiency, reducing the average query time by 93%. The web service built on top of the graph database provides programmatic access to Reactome data by object oriented queries, but also supports more complex queries that take advantage of the new underlying graph-based data storage. By adopting graph database technology we are providing a high performance pathway data resource to the community. The Reactome graph database use case shows the power of NoSQL database engines for complex biological data types.

Purpose – This paper aims to introduce a transformation engine which can be used to convert an existing institutional repository installation into a Linked Open Data repository. Design/methodology/approach – The authors describe how the data that exist in a DSpace repository can be semantically annotated to serve as a Semantic Web (meta)data repository. Findings – The authors present a non-intrusive, standards-compliant approach that can run alongside with current practices, while incorporating state-of-the art methodologies. Originality/value – Also, they propose a set of mappings between domain vocabularies that can be (re)used towards this goal, thus offering an approach that covers both the technical and semantic aspects of the procedure.

This case study engages students in the practice of civic data analysis by applying SQL to the Los Angeles Police Department's open crime datasets, covering the period from 2010 to the present. Participants step into the role of city analysts responsible for examining crime distribution, temporal rhythms, enforcement outcomes, and neighborhood variations. Through structured exercises, learners gain hands-on experience with SQL operations, including table creation, data cleaning, aggregation, and spatial joins. The project emphasizes how database queries can reveal actionable insights for policy discussions on public safety, community partnerships, and policing strategies. In doing so, students strengthen both their technical fluency in SQL and their ability to interpret real-world public data in a critical, applied context

The increasing availability of real-time in situ measurements and remote sensing observations have the potential to contribute to the optimisation of water resources management. Global challenges such as climate change, intensive agriculture and urbanisation put a high pressure on our water resources. Due to recent innovations in measuring both water quantity and quality, river systems can now be monitored in real time at an unprecedented spatial and temporal scale. To interpret the sensor measurements and remote sensing observations additional data, for example on the location of the measurement, and upstream and downstream catchment characteristics, are required. In this paper, we present a data management system to support flow-path-related functionality for decision making and prediction modelling. Adding meta-datasets and facilitating (near) real-time processing of sensor data questions are key concepts for the systems. The potential of the database framework for hydrological applications is demonstrated using different applications for the river system of Flanders. In one, the database framework is used to simulate the daily discharge for each segment within a catchment using a simple data-driven approach. The presented system is useful for numerous applications including pollution tracking, alerting and inter-sensor validation in river systems, or related networks.

Build faster, more reliable Rails apps by taking the best advanced PostgreSQL and Active Record capabilities, and using them to solve your application scale and growth challenges. Gain the skills needed to comfortably work with multi-terabyte databases, and with complex Active Record, SQL, and specialized Indexes. Develop your skills with PostgreSQL on your laptop, then take them into production, while keeping everything in sync. Make slow queries fast, perform any schema or data migration without errors, use scaling techniques like read/write splitting, partitioning, and sharding, to meet demanding workload requirements from Internet scale consumer apps to enterprise SaaS.Deepen your firsthand knowledge of high-scale PostgreSQL databases and Ruby on Rails applications with dozens of practical and hands-on exercises. Unlock the mysteries surrounding complex Active Record. Make any schema or data migration change confidently, without downtime. Grow your experience with modern and exclusive PostgreSQL features like SQL Merge, Returning, and Exclusion constraints. Put advanced capabilities like Full Text Search and Publish Subscribe mechanisms built into PostgreSQL to work in your Rails apps. Improve the quality of the data in your database, using the advanced and extensible system of types and constraints to reduce and eliminate application bugs. Tackle complex topics like how to improve query performance using specialized indexes. Discover how to effectively use built-in database functions and write your own, administer replication, and make the most of partitioning and foreign data wrappers. Use more than 40 well-supported open source tools to extend and enhance PostgreSQL and Ruby on Rails. Gain invaluable insights into database administration by conducting advanced optimizations - including high-impact database maintenance - all while solving real-world operational challenges. Take your new skills into production today and then take your PostgreSQL and Rails applications to a whole new level of reliability and performance.What You Need:A computer running macOS, Linux, or Windows and WSL2PostgreSQL version 16, installed by package manager, compiled, or running with DockerAn Internet connection

In developing NoSQL databases, a major motivation is to achieve better efficient query performance compared with relational databases. The graph database is a NoSQL paradigm where navigation is based on links instead of joining tables. Links can be implemented as pointers, and following a pointer is a constant time operation, whereas joining tables is more complicated and slower, even in the presence of foreign keys. Therefore, link-based navigation has been seen as a more efficient query approach than using join operations on tables. Existing studies strongly support this assumption. However, query complexity has received less attention. For example, in enterprise information systems, queries are usually complex so data need to be collected from several tables or by traversing paths of graph nodes of different types. In the present study, we compared the query performance of a graph-based database system (Neo4j) and relational database systems (MySQL and MariaDB). The effect of different efficiency issues (e.g., indexing and optimization) were included in the comparison in order to investigate the most efficient solutions for different query types. The outcome is that although Neo4j is more efficient for simple queries, MariaDB is essentially more efficient when the complexity of queries increases. The study also highlighted how dramatically the efficiency of relational database has grown during the last decade.

PostgreSQL is increasingly utilized in all kind of applications, starting from desktop to web and mobile applications. In this book, you will find the best ways to design, monitor and maintain your PostgreSQL solution, with suggestions and tips for high performance, troubleshooting and high availability.

In 2002, Agrawal and Kiernan defined six basic requirements, including preventing illegal watermark embedding and authentication, reversibility, robustness, and others, which must be satisfied when a reversible watermark is designed for relational databases. To meet these requirements, in this paper, a lossless watermarking scheme for a categorical relational database called LRW-CRDB (lossless robust watermarking for categorical relational databases) is proposed. In our LRW-CRDB scheme, the database owner needs to generate two secret embedding keys, K1 and K2, in advance. Then, two reference sets are generated based on two different secret embedding keys and a symmetry-based data hiding strategy, and then these are used for the watermark embedding phases. Experimental results confirmed that our LRW-CRDB scheme successfully detects 100% of hidden watermarks, even when more than 95% of the watermarked relational database has been deleted. In other words, the robustness of our proposed LRW-CRDB scheme outperforms other existing schemes under a variety of possible attacks, such as alteration, sorting, deletion, and mix-match attacks.

Data encryption‐based and secret sharing‐based data outsourcing schemes protect the confidentiality of sensitive attributes but not their secrecy. Ciphertexts/shares generated by a data encryption/secret sharing scheme can attract the attention of interceptors. Thus, it is desired to hide the existence of highly‐sensitive attributes (as secret attributes) in the outsourced relations in addition to protecting their contents. This paper proposes a novel scheme that integrates data hiding with secret sharing for relational databases to protect both the secrecy and confidentiality of secret attributes. It embeds one or multiple secret attributes in a relation into one or multiple cover attributes in the same relation. A set of share (and possibly index) columns are constructed such that they are pretended to be associated with only the cover attributes, while those share columns and some virtual share columns can be used to recover both the secret and cover attributes. What interceptors observe in each relation include the attributes stored in plaintext and the share (and possibly index) columns associated with the cover attributes but not any extra column. Thus, they find nothing suspicious. This is the first effective data hiding scheme for relational databases that protects the secrecy of secret attributes. This paper proposes a novel scheme that integrates data hiding with secret sharing for relational databases to protect both secrecy and confidentiality of secret attributes. To the best of our knowledge, this is the first work that protects the secrecy of secret attributes in relational databases by using data hiding. In addition, it protects the confidentiality of the hidden attributes using secret sharing.